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Analytics

RFM is a method used for analyzing customer value. It is commonly used in database marketing and direct marketing and has received particular attention in the casino and retail industries. RFM stands for:

Recency—How recently did the customer purchase?

Frequency—How often do they purchase?

Monetary Value—How much do they spend?

Most businesses will keep scores of data about a customer’s purchases. All that is needed is a table with the customer name, date of purchase and purchase value. One methodology is to assign a scale of 1 to 10, whereby 10 is the maximum value and to stipulate a formula by which the data suits the scale. For example, in a service based business like the casino business, you could have the following:

Recency = 10—the number of months that have passed since the customer last purchased.

Frequency = number of purchases in the last 12 months (maximum of 10).

Monetary = value of the highest order from a given customer (benchmarked against $10k).

Alternatively, one can create categories for each attribute. For instance, the ‘Recency’ attribute might be broken into three categories: customers with purchases within the last 90 days; purchases between 91 and 365 days; and purchases longer than 365 days. Such categories may be arrived at by applying business rules, or using a data mining technique to find meaningful breaks.

Once each of the attributes has appropriate categories defined, segments are created from the intersection of the values. If there were three categories for each attribute, then the resulting matrix would have twenty-seven possible combinations (one well-known commercial approach uses five bins per attribute, which yields 125 segments).

Segments could also be collapsed into sub-segments, if the gradations appear too small to be useful. The resulting segments can be ordered from most valuable (highest recency, frequency, and value) to least valuable (lowest recency, frequency, and value). Identifying the most valuable RFM segments can capitalize on chance relationships in the data used for this analysis. For this reason, it is highly recommended that another set of data be used to validate the results of the RFM segmentation process.

Advocates of this technique point out that it has the virtue of simplicity: no specialized statistical software is required, and the results are readily understood by business people. In the absence of other targeting techniques, it can provide a lift in response rates for promotions.

Whichever approach is adopted, profiling will be done on the final results to determine what makes up group membership. Categorical factors such as gender, nationality/locality can be used as well as age (or, indeed, any other demographic feature that is available) to understand the “type” of customer that resides in each group. These factors can be used for each segment and applied against the population metrics to determine how much more or less likely a segment is to exhibit a particular feature or type of behavior when compared to the customer base as a whole.

A few words of caution in the gaming field: a major drawback of classical RFM modeling is the high propensity of a casino and/or a sports book to be continually hitting the same segment(s) with the same marketing message.

A Propensity to Response model is the theoretical probability that a sampled person (or unit) will become a respondent in an offer or survey. They are especially useful in the marketing field.

A response likelihood model can have substantial cost savings as it can lead to lower mailing costs by identifying patrons who are very unlikely to respond to a particular offer. After segmenting these people out, the casino can then focus on only those most likely to take up the offer. A casino can identify the likelihood of response from all eligible patrons.After that, it can identify the most valuable patrons that are most likely to respond. This allows the casino to estimate the expected response from the most valuable patrons and eliminate mailing(s) to the patrons that are of lower worth and/or are unlikely to respond.

Occasionally, response likelihood models will lead to easy decisions, such as cutting out low worth patrons with a low likelihood of responding. However, more complex situations might arise since response models are never perfect. It doesn’t matter how good a model is or how accurate the historical data is, there is always a chance that a patron identified as unlikely to respond will respond. Thus, when making a decision about patrons identified as unlikely to respond to an offer, it is also important to balance that likelihood of response with the potential return on response.

A propensity to respond model would be built using historical information around marketing campaigns and it looks at predicting the likelihood a customer will respond to a marketing communication. The advantage of this model is that it strengthens the marketing strategy even more, beyond purely segmenting the customer base. It can further allow for improved ROI on the marketing budget, by identifying the likely number of respondents to be returned by a campaign.

Often a business’ marketing department will have an expected number of respondents or an expected response rate. By identifying those who are most likely to respond, the chances of meeting that expected number or rate of response is greatly improved. Gone are the days of marketing to an entire customer base. This is an unnecessary waste of the marketing budget and also runs the risk of annoying customers by touching them too often or with the wrong offer.

Again, a predictive model could be built which identifies those most likely to respond through to those least likely to respond. This would be done using customer metrics and historical campaign/marketing information that identifies those who responded and those who didn’t. Variables that have a significant association with the customer action are extracted and these form part of the prediction algorithm. Every customer is then given a score according to how likely they are to respond to a marketing campaign.

In addition to predicting the future worth of patrons, it is important to know which marketing campaigns are the most effective for driving response, revenue, and profit. In general, certain offers are better than others, and specifically certain offers will be better for certain patrons.

While knowing the probable future worth of a patron is critical for determining the reinvestment level for which a patron is eligible, patrons’ behaviors and interests can be used to identify the offer(s) that will be most appealing to each patron as well as the ones generating the most profitable response. By analyzing the likelihood that a patron will respond to a certain offer or offers, sports book analysts can optimize the offer that each patron is given in order to maximize the amount of revenue and profit driven by the marketing campaigns as a whole.

A/B testing is one of the best ways to identify which offers work best. A/B testing involves testing two different offers against one another in order to identify the offer that drives the highest response and the most revenue/profit. More advanced statistical methods can be used to generate likelihood of response scores and classification scores. Some of the more common statistical approaches are logistic regression, decision trees, and discriminant analysis.

Essentially, these statistical methods use historical data to find the factors that are related as to why a patron responds. Those factors can then be used to assess the likelihood of response based on the similarity of a patron profile to that of responders.

These methods have historically been used in direct marketing analysis to identify the best types of offers and the most likely responders. In order to build accurate and predictive response models, historical data about response is required. The likelihood of response might be a broad measure of response that refers to the likelihood a patron will respond to any offer, or it might be specific to the likelihood of response to a specific type of offer.

It’s a good idea to select test segments of customers for the purpose of continually testing new offers. Doing so will help to ensure that there is a large amount of response data that can be used to build models and continually improve the efficacy of marketing. Effective response models will help identify which patrons are most likely to respond to an offer, and in turn to which offer patrons are most likely to respond.

A customer segmentation model provides a view of the casino from a customer perspective: such models have many and varied applications. Customers are segmented according to what they present to the casino. Views include:

Game preference

Day of week

Time of day

Length of session

Size of stake

Generally, the data is used to determine the appropriate segments for these views. However, the casino has the ability to select the intervals that are preferential and relevant to their venue. For example, it may be desired to split time of day into three, eight-hour periods or six, four-hour periods.

The results of this analysis presents a detailed view of how the casino is populated at different times and can allow for appropriate strategic decisions to be made. These decisions could be a function of marketing, operations, or strategy. The output is also used for the building of acquisition models as discussed below.

Other potential for analysis would be a master segmentation model that uses the preference results described. Customers are clustered based on their preferences to gain a global view of the casino that is concise and understandable. Furthermore, such models can help measure the impact of strategic decisions, e.g. the addition or removal of a game can be measured against how particular metrics are affected.

The Customer Conversion Model can be used to score customers based on information contained in the casino’s source systems as it would only be applicable for customers who had pre-booked their room (as opposed to walk-in customers). Historical information would be extracted from the casino’s IT systems around desirable customers. This would include spending patterns and profitability.nTo identify the relationships that may exist between how the customer comes to the casino and his or her desirability metric, information would be extracted from the casino’s source systems. For a casino, this would include information such as source of betting, channel of betting, lead-time for betting and the incentives offered to attract the customer. Basically, anything that can be attributed to the initial transaction the customer has with the casino would be used as a potential input.

These models might also have to be stratified by itinerary to identify the most relevant relationships. The major advantage of a predictive model with this intention would be that it allows the casino to identify customers that they need to interact with once they step onto the casino floor. This would give the casino hosts the potential to get the required information they need to successfully foster a strong and lasting customer relationship.

Furthermore, if every potential customer has a score associated with him or her as to his or her long-term likelihood of being attractive, the casino can further hone in on its customers by monitoring their behavior once they are on the casino floor. It is imperative that the casino interact with desirable customers before they have left the property. If customers are made to feel like they are valuable and worthwhile, the likelihood of them returning under their own volition significantly increases. a baseline for customer ROI get also be set at this time, something that can help with marketing expense as the relationship grows.

Arguably, customer retention is both one of the cornerstones of any CRM system, as well as being the most important component of the customer lifetime value (CLV) framework. There are indications that companies have problems managing customer retention. Casinos are unique from many other industries in that their customers are tied into contracts, but many of the retention metrics relevant for contractual firms are also relevant for non-contractual firms. A simple 0/1 indicator of transaction, and a measure of recency are appropriate for both types of companies.

Intelligencia proposes the following process to develop and evaluate a single retention campaign:

1.Identify customers who are at risk of not being retained.

2.Diagnose why each customer is at risk.

3.Decide when to target these customers and with what incentive and/or action.

4.Implement the campaign and evaluate it.

These steps are applicable to both proactive and reactive campaigns. Reactive campaigns are simpler because the firm doesn’t need to identify who is at risk—the customer who calls to cancel self-identifies. ‘Rescue rates’ can readily be calculated to evaluate the program, and subsequent behavior can be monitored. The incentive should be substantial because the company is pretty certain the customer will churn. Reactive campaigns, however, can be challenging because not all customers can be rescued, and, because we’re dealing with human nature here, customers learn that informing the firm about their intentions to churn can be richly rewarded with valuable incentive, which can endanger the long-run sustainability of reactive churn management.

Proactive campaigns are more challenging starting from the basic task of identifying who is at risk. Balancing the cost of false positives (targeting a customer who has no intention to leave) against false negatives (failing to identify a customer who is truly at risk) requires sophisticated analytics.

To discover who is at risk, a predictive model must be built that identifies customers at risk of not being retained, or in general of generating lower retention metrics. The dependent variable could be 0/1 churn or any measure of retention. Table 1 summarizes variables predictor variables for several different industries, all in contractual settings, but many will be useful for the casino industry.

Factors

Example

Method

Customer Satisfaction

1. Emotion in emails

1. Logistic, SVM, Random Forests

2. Customer service calls

2. SVM + ALBA

3. Usage trends

3. Logistic, NN, SVM, Genetic

4. Complaints

4. Logistic, NN, SVM, Genetic

5. Previous non-renewal

5. Logistic, SVM, Random Forests

Usage Behavior

1. Usage levels

1. SVM with ALBA

2. Usage levels

2. Logistic, NN, SVM, Genetic

Switching Costs

1. Add-on services

1. Logistic, NN, SVM, Genetic

2. Pricing plan

2. Dec Tree, Naïve Bayes, Logistic, NN, SVM

3. Ease of switching

3. Graphical comparison

Customer Characteristics

1. Psychographic Segment

1. Logistic, NN, SVM, Genetic

2. Demographics

2. Logistic, NN, SVM, Genetic

3. Customer tenure

3. Logistic, Decision Tree

Marketing

1. Mail responders

1. Bagging and Boosting

2. Response to direct mail

2. Logistic, SVM, Random Forests

3. Previous marketing campaigns

3. Decision rules

4. Acquisition method

4. Probit

5. Acquisition channel

5. Logistic

Social Connectivity

1. Neighbor churn

1. Hazard

2. Social network connections

2. Random Forests, Bayesian Networks

3. Social embeddedness

3. Decision rules

4. Neighbor/connections usage

4. Logistic

Table 1: Predictors of Churn in Contractual Settings

Source: Ascarza, Neslin, Netzer, Lemmens, Aurelie1

The main goal of a retention program is obviously to prevent churn, therefore understanding the causes of such churn behavior is imperative if you are to design an effective retention program.To identify the potential causes of churn for an individual customer, the variables or combinations of variables that are both viable causes and for which the customer exhibits a risky behavior must be discovered. A competing risk hazard model could be used to predict which of the possible reasons of churn are most likely to cause churn at any point in time. Once the causes of churn are identified, the casino needs to isolate those that are are controllable and those who are not. Both correlates of low retention and also the causes of it need to be identified.

To ensure customer retention is front and center, casinos should be scoring their databases on a regular basis in order to understand the likelihood of a customer churning from their venue. This kind of modeling is prevalent in the telecommunications, finance, and utilities industries, and should be utilized in the gaming industry as well. While a slightly different set up due to those industries mostly having their customers locked into contracts, gaming companies need to stay ahead of the game in retaining their customers.

Anecdotal evidence collected in our discussions with gaming companies have indicated a tendency to ignore customers until they have not been seen for up to two years. At this stage, there might be a marketing activity targeted at the customer for up to 12 months. It could be proffered that, by this stage, it is too late to win the customer back; the customer has probably already made up his or her mind and, once a decision like that has been made, it is almost impossible to reverse it, no matter how attractive any competing offer might be.

One of the hardest parts for a gaming company to determine—as opposed to commercial entities that have their customers on contract and definitely know they are tied down—is whether the customer has categorically churned. It may be that a change in location, circumstances or something else has caused a customer to disappear from the sports book, with every intention of returning. However, statistical measures could be used to identify customer’s whose behavior has changed and the change wouldn’t be attributed to chance.

Historical internal data can be used to model the difference between a churned customer and one who is still engaged. There would be significant metrics in the data that identify the likelihood of churning. Similar to the acquisition model described above, a parametric equation could be constructed that elicits the association and relationship between the target variable and the predictors.

This model would serve as an early warning system for the sports book. It would also be a strategic tool useful to predict whether a customer was deemed worth retaining or not. The model should be run on a regular basis across the entire customer database to understand which customers have reached or are reaching a critical value in their churnscore. The theory: these customers would then be targeted with an offer to return to the sports book, in the process avoiding the likelihood of them churning. Alternatively, if the customer is deemed to be of little or no value, there would be no offer forthcoming to entice them to return.

Just like every other business, casino operators are always looking for new customers. With the gaming market becoming more and more competitive and saturated by the day, there is always a constant need to attract new customers. Customer segmentation models can be used to build predictive models that identify key characteristics of attractive customers.

Obviously, a casino will have no internal data available on customers they don’t already have on their books, so the analysis becomes a data mining exercise using publicly available input variables. Casinos can then target these customers with a view to attracting those who have the traits that they see in their already valuable customers. The best external data to use would be population census data, linked to the internal customers by a location identifier (such as postcode or mesh block). It is acknowledged that in some jurisdictions robust and accurate census data may not be available so the model would be relying on whatever information the casino records on its customers from a demographic and lifestyle point of view.

This approach becomes a classical data-mining problem, where a pool of independent variables are tested for the strength of association with the response variable. Once the relevant predictors are identified and the characteristics and traits are defined, marketing and acquisition campaigns can be targeted at the population towards these kinds of people. This would be something that looks to predict a metric derived from current/past customers. Such a metric could come from a segmentation model that identified the high value customers that are most attractive to the gaming company.

There are several approaches that can be used and once the target has been defined, this allows for a parametric equation to be derived. This equation attempts to predict the characteristics that distinguish the desirable customers from the rest. This model can only use publicly available information (although other casino information might be acceptable) as that is how a potential customer would be identified. Current information that the company would have on hand would be age, nationality, gender, and address. Where available, third party data should be looked at to further enhance the findings. This could be census data that gives an indication of further customer demographics and this enhances the ability to hone in on customer sweet spots. Data from our data broker partners can also be tapped.